Overview

Dataset statistics

Number of variables13
Number of observations20707
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.1 MiB
Average record size in memory104.0 B

Variable types

Categorical2
Numeric11

Alerts

id has a high cardinality: 20707 distinct values High cardinality
num_nodes is highly correlated with num_tweets and 2 other fieldsHigh correlation
num_tweets is highly correlated with num_nodes and 2 other fieldsHigh correlation
avg_num_retweet is highly correlated with retweet_perc and 2 other fieldsHigh correlation
retweet_perc is highly correlated with avg_num_retweet and 1 other fieldsHigh correlation
num_users is highly correlated with num_nodes and 2 other fieldsHigh correlation
avg_num_followers is highly correlated with avg_num_retweet and 2 other fieldsHigh correlation
avg_num_friends is highly correlated with avg_num_followersHigh correlation
avg_time_diff is highly correlated with avg_num_retweet and 2 other fieldsHigh correlation
users_10h is highly correlated with num_nodes and 2 other fieldsHigh correlation
num_nodes is highly correlated with num_tweets and 2 other fieldsHigh correlation
num_tweets is highly correlated with num_nodes and 2 other fieldsHigh correlation
avg_num_retweet is highly correlated with retweet_percHigh correlation
retweet_perc is highly correlated with avg_num_retweetHigh correlation
num_users is highly correlated with num_nodes and 2 other fieldsHigh correlation
users_10h is highly correlated with num_nodes and 2 other fieldsHigh correlation
num_nodes is highly correlated with num_tweets and 2 other fieldsHigh correlation
num_tweets is highly correlated with num_nodes and 2 other fieldsHigh correlation
avg_num_retweet is highly correlated with retweet_perc and 1 other fieldsHigh correlation
retweet_perc is highly correlated with avg_num_retweetHigh correlation
num_users is highly correlated with num_nodes and 2 other fieldsHigh correlation
avg_time_diff is highly correlated with avg_num_retweetHigh correlation
users_10h is highly correlated with num_nodes and 2 other fieldsHigh correlation
label is highly correlated with retweet_percHigh correlation
num_nodes is highly correlated with num_tweets and 2 other fieldsHigh correlation
num_tweets is highly correlated with num_nodes and 2 other fieldsHigh correlation
avg_num_retweet is highly correlated with retweet_percHigh correlation
retweet_perc is highly correlated with label and 2 other fieldsHigh correlation
num_users is highly correlated with num_nodes and 2 other fieldsHigh correlation
avg_num_followers is highly correlated with avg_num_friendsHigh correlation
avg_num_friends is highly correlated with avg_num_followersHigh correlation
perc_post_1_hour is highly correlated with retweet_percHigh correlation
users_10h is highly correlated with num_nodes and 2 other fieldsHigh correlation
avg_num_retweet is highly skewed (γ1 = 24.90205017) Skewed
avg_num_followers is highly skewed (γ1 = 43.04629615) Skewed
avg_time_diff is highly skewed (γ1 = 21.51128756) Skewed
id is uniformly distributed Uniform
id has unique values Unique
avg_num_retweet has 11573 (55.9%) zeros Zeros
avg_time_diff has 11586 (56.0%) zeros Zeros

Reproduction

Analysis started2021-11-08 10:06:07.069668
Analysis finished2021-11-08 10:06:51.907060
Duration44.84 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

label
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size161.9 KiB
real
15648 
fake
5059 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowfake
2nd rowfake
3rd rowfake
4th rowfake
5th rowfake

Common Values

ValueCountFrequency (%)
real15648
75.6%
fake5059
 
24.4%

Length

2021-11-08T15:36:52.326771image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-11-08T15:36:52.704980image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
real15648
75.6%
fake5059
 
24.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

num_nodes
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct908
Distinct (%)4.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean83.51567103
Minimum2
Maximum4494
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size161.9 KiB
2021-11-08T15:36:52.958475image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile2
Q115
median40
Q365
95-th percentile212.7
Maximum4494
Range4492
Interquartile range (IQR)50

Descriptive statistics

Standard deviation228.0398023
Coefficient of variation (CV)2.730503144
Kurtosis62.58983601
Mean83.51567103
Median Absolute Deviation (MAD)25
Skewness7.06566585
Sum1729359
Variance52002.15144
MonotonicityNot monotonic
2021-11-08T15:36:53.403447image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
21216
 
5.9%
3469
 
2.3%
7343
 
1.7%
5341
 
1.6%
4341
 
1.6%
6338
 
1.6%
10326
 
1.6%
15316
 
1.5%
9312
 
1.5%
12300
 
1.4%
Other values (898)16405
79.2%
ValueCountFrequency (%)
21216
5.9%
3469
 
2.3%
4341
 
1.6%
5341
 
1.6%
6338
 
1.6%
7343
 
1.7%
8299
 
1.4%
9312
 
1.5%
10326
 
1.6%
11288
 
1.4%
ValueCountFrequency (%)
44941
< 0.1%
35831
< 0.1%
35051
< 0.1%
34551
< 0.1%
32201
< 0.1%
32001
< 0.1%
31931
< 0.1%
30701
< 0.1%
29411
< 0.1%
29391
< 0.1%

num_tweets
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct667
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean58.47336649
Minimum1
Maximum1730
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size161.9 KiB
2021-11-08T15:36:53.679623image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q112
median36
Q358
95-th percentile141
Maximum1730
Range1729
Interquartile range (IQR)46

Descriptive statistics

Standard deviation121.1044079
Coefficient of variation (CV)2.071103737
Kurtosis36.98730366
Mean58.47336649
Median Absolute Deviation (MAD)23
Skewness5.663174855
Sum1210808
Variance14666.2776
MonotonicityNot monotonic
2021-11-08T15:36:53.947256image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11312
 
6.3%
2507
 
2.4%
3435
 
2.1%
4386
 
1.9%
5378
 
1.8%
6347
 
1.7%
9330
 
1.6%
14324
 
1.6%
8318
 
1.5%
11306
 
1.5%
Other values (657)16064
77.6%
ValueCountFrequency (%)
11312
6.3%
2507
 
2.4%
3435
 
2.1%
4386
 
1.9%
5378
 
1.8%
6347
 
1.7%
7297
 
1.4%
8318
 
1.5%
9330
 
1.6%
10289
 
1.4%
ValueCountFrequency (%)
17301
< 0.1%
16321
< 0.1%
15851
< 0.1%
15511
< 0.1%
15161
< 0.1%
14141
< 0.1%
14012
< 0.1%
13421
< 0.1%
13192
< 0.1%
12781
< 0.1%

avg_num_retweet
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED
ZEROS

Distinct2969
Distinct (%)14.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2229082209
Minimum0
Maximum51
Zeros11573
Zeros (%)55.9%
Negative0
Negative (%)0.0%
Memory size161.9 KiB
2021-11-08T15:36:54.277708image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.148474567
95-th percentile1.038843385
Maximum51
Range51
Interquartile range (IQR)0.148474567

Descriptive statistics

Standard deviation0.9019846535
Coefficient of variation (CV)4.046439606
Kurtosis1071.340429
Mean0.2229082209
Median Absolute Deviation (MAD)0
Skewness24.90205017
Sum4615.760529
Variance0.8135763151
MonotonicityNot monotonic
2021-11-08T15:36:54.641994image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
011573
55.9%
0.3333333333171
 
0.8%
1163
 
0.8%
0.2159
 
0.8%
0.5151
 
0.7%
0.25137
 
0.7%
0.1666666667123
 
0.6%
0.111111111195
 
0.5%
0.142857142988
 
0.4%
0.186
 
0.4%
Other values (2959)7961
38.4%
ValueCountFrequency (%)
011573
55.9%
0.0048076923081
 
< 0.1%
0.0054347826091
 
< 0.1%
0.0057803468211
 
< 0.1%
0.0057870370371
 
< 0.1%
0.0062695924761
 
< 0.1%
0.0063291139241
 
< 0.1%
0.0065359477121
 
< 0.1%
0.0071428571431
 
< 0.1%
0.0074074074071
 
< 0.1%
ValueCountFrequency (%)
511
< 0.1%
47.51
< 0.1%
341
< 0.1%
271
< 0.1%
251
< 0.1%
20.51
< 0.1%
201
< 0.1%
181
< 0.1%
17.41
< 0.1%
16.51
< 0.1%

retweet_perc
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3082
Distinct (%)14.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.173209762
Minimum0.003125
Maximum0.980952381
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size161.9 KiB
2021-11-08T15:36:55.066746image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.003125
5-th percentile0.01538461538
Q10.02777777778
median0.07692307692
Q30.2727272727
95-th percentile0.5394736842
Maximum0.980952381
Range0.977827381
Interquartile range (IQR)0.2449494949

Descriptive statistics

Standard deviation0.191102693
Coefficient of variation (CV)1.103302093
Kurtosis0.8439326292
Mean0.173209762
Median Absolute Deviation (MAD)0.05805515239
Skewness1.314962197
Sum3586.654542
Variance0.03652023929
MonotonicityNot monotonic
2021-11-08T15:36:55.508047image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.51395
 
6.7%
0.3333333333574
 
2.8%
0.25400
 
1.9%
0.2339
 
1.6%
0.1666666667305
 
1.5%
0.1428571429284
 
1.4%
0.1111111111271
 
1.3%
0.06666666667268
 
1.3%
0.1267
 
1.3%
0.05555555556253
 
1.2%
Other values (3072)16351
79.0%
ValueCountFrequency (%)
0.0031251
< 0.1%
0.004629629631
< 0.1%
0.0055248618781
< 0.1%
0.0058479532161
< 0.1%
0.0058823529411
< 0.1%
0.0060975609761
< 0.1%
0.0062893081761
< 0.1%
0.006410256411
< 0.1%
0.0067567567571
< 0.1%
0.0068027210882
< 0.1%
ValueCountFrequency (%)
0.9809523811
< 0.1%
0.97959183671
< 0.1%
0.97183098591
< 0.1%
0.96491228071
< 0.1%
0.9629629631
< 0.1%
0.95454545452
< 0.1%
0.951
< 0.1%
0.94623655911
< 0.1%
0.94444444441
< 0.1%
0.94366197181
< 0.1%

num_users
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct840
Distinct (%)4.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean69.04882407
Minimum1
Maximum3071
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size161.9 KiB
2021-11-08T15:36:55.895043image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q111
median35
Q354
95-th percentile180.7
Maximum3071
Range3070
Interquartile range (IQR)43

Descriptive statistics

Standard deviation183.0438335
Coefficient of variation (CV)2.650933394
Kurtosis57.27780374
Mean69.04882407
Median Absolute Deviation (MAD)22
Skewness6.827869215
Sum1429794
Variance33505.04499
MonotonicityNot monotonic
2021-11-08T15:36:56.223264image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11290
 
6.2%
2559
 
2.7%
5444
 
2.1%
3417
 
2.0%
4402
 
1.9%
9393
 
1.9%
8388
 
1.9%
7360
 
1.7%
6358
 
1.7%
11357
 
1.7%
Other values (830)15739
76.0%
ValueCountFrequency (%)
11290
6.2%
2559
2.7%
3417
 
2.0%
4402
 
1.9%
5444
 
2.1%
6358
 
1.7%
7360
 
1.7%
8388
 
1.9%
9393
 
1.9%
10333
 
1.6%
ValueCountFrequency (%)
30711
< 0.1%
28801
< 0.1%
28771
< 0.1%
26551
< 0.1%
24481
< 0.1%
23951
< 0.1%
23641
< 0.1%
23231
< 0.1%
23131
< 0.1%
22901
< 0.1%

total_propagation_time
Real number (ℝ≥0)

Distinct20415
Distinct (%)98.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1513952418
Minimum1210434286
Maximum1545330351
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size161.9 KiB
2021-11-08T15:36:56.786368image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1210434286
5-th percentile1493292819
Q11504002795
median1515167137
Q31524636878
95-th percentile1533539483
Maximum1545330351
Range334896065
Interquartile range (IQR)20634083.5

Descriptive statistics

Standard deviation14860078.73
Coefficient of variation (CV)0.009815419926
Kurtosis50.16884292
Mean1513952418
Median Absolute Deviation (MAD)10181844
Skewness-3.339417445
Sum3.134941272 × 1013
Variance2.208219399 × 1014
MonotonicityNot monotonic
2021-11-08T15:36:57.059805image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
153599309112
 
0.1%
153559569811
 
0.1%
15220964096
 
< 0.1%
15363646955
 
< 0.1%
15353678625
 
< 0.1%
15306140294
 
< 0.1%
15361571574
 
< 0.1%
15363058684
 
< 0.1%
15230398344
 
< 0.1%
15200628744
 
< 0.1%
Other values (20405)20648
99.7%
ValueCountFrequency (%)
12104342861
< 0.1%
12186090421
< 0.1%
12239148411
< 0.1%
12462194221
< 0.1%
12672166721
< 0.1%
12737574581
< 0.1%
12893316281
< 0.1%
13100678551
< 0.1%
13119022831
< 0.1%
13123398761
< 0.1%
ValueCountFrequency (%)
15453303511
 
< 0.1%
15452704711
 
< 0.1%
15449689411
 
< 0.1%
15449605571
 
< 0.1%
15449585773
< 0.1%
15449408201
 
< 0.1%
15448792661
 
< 0.1%
15448739501
 
< 0.1%
15448494711
 
< 0.1%
15448186231
 
< 0.1%

avg_num_followers
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
SKEWED

Distinct19405
Distinct (%)93.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean43606.33895
Minimum0
Maximum11710354
Zeros7
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size161.9 KiB
2021-11-08T15:36:57.487158image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile513.1
Q11912.9375
median4189.958333
Q340927.0625
95-th percentile208577.1552
Maximum11710354
Range11710354
Interquartile range (IQR)39014.125

Descriptive statistics

Standard deviation131890.3209
Coefficient of variation (CV)3.024567621
Kurtosis3329.7346
Mean43606.33895
Median Absolute Deviation (MAD)3077.958333
Skewness43.04629615
Sum902956460.7
Variance1.739505674 × 1010
MonotonicityNot monotonic
2021-11-08T15:36:57.811568image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3937342
 
1.7%
1329177
 
0.9%
1112139
 
0.7%
31772977
 
0.4%
4877
 
0.4%
927
 
0.1%
2524.517
 
0.1%
1992.517
 
0.1%
68.9545454517
 
0.1%
315
 
0.1%
Other values (19395)19802
95.6%
ValueCountFrequency (%)
07
< 0.1%
112
0.1%
1.0555555561
 
< 0.1%
29
< 0.1%
315
0.1%
42
 
< 0.1%
4.7333333331
 
< 0.1%
51
 
< 0.1%
64
 
< 0.1%
71
 
< 0.1%
ValueCountFrequency (%)
117103541
< 0.1%
58607081
< 0.1%
58552491
< 0.1%
2295190.61
< 0.1%
16943951
< 0.1%
14599461
< 0.1%
13066831
< 0.1%
1172399.21
< 0.1%
1066707.2731
< 0.1%
953163.6251
< 0.1%

avg_num_friends
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct19131
Distinct (%)92.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2332.05777
Minimum0
Maximum111645
Zeros109
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size161.9 KiB
2021-11-08T15:36:58.180444image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile406.9269231
Q11142.198684
median1784.2
Q32854.25
95-th percentile5712.586218
Maximum111645
Range111645
Interquartile range (IQR)1712.051316

Descriptive statistics

Standard deviation2583.053995
Coefficient of variation (CV)1.107628648
Kurtosis383.8004211
Mean2332.05777
Median Absolute Deviation (MAD)774.0545455
Skewness13.38117773
Sum48289920.25
Variance6672167.943
MonotonicityNot monotonic
2021-11-08T15:36:58.507569image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1128342
 
1.7%
1496177
 
0.9%
69141
 
0.7%
0109
 
0.5%
72380
 
0.4%
56422
 
0.1%
104.181818217
 
0.1%
598.517
 
0.1%
22116
 
0.1%
2716
 
0.1%
Other values (19121)19770
95.5%
ValueCountFrequency (%)
0109
0.5%
18
 
< 0.1%
25
 
< 0.1%
31
 
< 0.1%
53
 
< 0.1%
5.41
 
< 0.1%
61
 
< 0.1%
6.3333333331
 
< 0.1%
71
 
< 0.1%
8.51
 
< 0.1%
ValueCountFrequency (%)
1116451
< 0.1%
931331
< 0.1%
84111.166671
< 0.1%
75835.51
< 0.1%
71258.571431
< 0.1%
56129.51
< 0.1%
558361
< 0.1%
55126.888891
< 0.1%
468631
< 0.1%
465731
< 0.1%

avg_time_diff
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
SKEWED
ZEROS

Distinct8108
Distinct (%)39.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean63317.38665
Minimum-3823143.75
Maximum25733526
Zeros11586
Zeros (%)56.0%
Negative1
Negative (%)< 0.1%
Memory size161.9 KiB
2021-11-08T15:36:58.835521image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum-3823143.75
5-th percentile0
Q10
median0
Q37690.015625
95-th percentile116886.7906
Maximum25733526
Range29556669.75
Interquartile range (IQR)7690.015625

Descriptive statistics

Standard deviation627812.3568
Coefficient of variation (CV)9.915323263
Kurtosis575.8213057
Mean63317.38665
Median Absolute Deviation (MAD)0
Skewness21.51128756
Sum1311113125
Variance3.941483553 × 1011
MonotonicityNot monotonic
2021-11-08T15:36:59.186978image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
011586
56.0%
2912
 
0.1%
112
 
0.1%
5711
 
0.1%
6311
 
0.1%
2310
 
< 0.1%
419
 
< 0.1%
309
 
< 0.1%
619
 
< 0.1%
398
 
< 0.1%
Other values (8098)9030
43.6%
ValueCountFrequency (%)
-3823143.751
 
< 0.1%
011586
56.0%
112
 
0.1%
53
 
< 0.1%
75
 
< 0.1%
82
 
< 0.1%
92
 
< 0.1%
101
 
< 0.1%
10.51
 
< 0.1%
111
 
< 0.1%
ValueCountFrequency (%)
257335261
< 0.1%
21256803.931
< 0.1%
199119931
< 0.1%
194867831
< 0.1%
186330301
< 0.1%
185206791
< 0.1%
180901511
< 0.1%
163554521
< 0.1%
163554321
< 0.1%
145418051
< 0.1%

perc_post_1_hour
Real number (ℝ≥0)

HIGH CORRELATION

Distinct3711
Distinct (%)17.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.4048516835
Minimum0.0002790957298
Maximum1.063829787
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size161.9 KiB
2021-11-08T15:36:59.583067image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.0002790957298
5-th percentile0.05
Q10.2
median0.347826087
Q30.5769230769
95-th percentile0.9130434783
Maximum1.063829787
Range1.063550692
Interquartile range (IQR)0.3769230769

Descriptive statistics

Standard deviation0.2559022829
Coefficient of variation (CV)0.632088968
Kurtosis-0.588257315
Mean0.4048516835
Median Absolute Deviation (MAD)0.172826087
Skewness0.5388585871
Sum8383.263811
Variance0.06548597837
MonotonicityNot monotonic
2021-11-08T15:36:59.920140image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.51715
 
8.3%
0.3333333333610
 
2.9%
0.25442
 
2.1%
0.6666666667392
 
1.9%
0.2363
 
1.8%
0.1666666667251
 
1.2%
0.4235
 
1.1%
0.1428571429194
 
0.9%
0.6166
 
0.8%
0.2857142857165
 
0.8%
Other values (3701)16174
78.1%
ValueCountFrequency (%)
0.00027909572981
< 0.1%
0.00031318509241
< 0.1%
0.00036416605971
< 0.1%
0.00037993920971
< 0.1%
0.00055648302731
< 0.1%
0.00060277275471
< 0.1%
0.0006377551021
< 0.1%
0.00064474532561
< 0.1%
0.00066269052351
< 0.1%
0.00079333597781
< 0.1%
ValueCountFrequency (%)
1.0638297871
< 0.1%
0.99941927991
< 0.1%
0.99921813921
< 0.1%
0.99919289751
< 0.1%
0.99911738751
< 0.1%
0.99910071942
< 0.1%
0.99906279291
< 0.1%
0.99902056811
< 0.1%
0.99900695131
< 0.1%
0.9991
< 0.1%

users_10h
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct486
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38.91659825
Minimum1
Maximum1204
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size161.9 KiB
2021-11-08T15:37:00.187202image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q17
median27
Q346
95-th percentile92
Maximum1204
Range1203
Interquartile range (IQR)39

Descriptive statistics

Standard deviation71.75327149
Coefficient of variation (CV)1.843770389
Kurtosis59.75803917
Mean38.91659825
Median Absolute Deviation (MAD)19
Skewness6.800165365
Sum805846
Variance5148.53197
MonotonicityNot monotonic
2021-11-08T15:37:00.554729image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12180
 
10.5%
2823
 
4.0%
3621
 
3.0%
4486
 
2.3%
5433
 
2.1%
7410
 
2.0%
39406
 
2.0%
41401
 
1.9%
8400
 
1.9%
9394
 
1.9%
Other values (476)14153
68.3%
ValueCountFrequency (%)
12180
10.5%
2823
 
4.0%
3621
 
3.0%
4486
 
2.3%
5433
 
2.1%
6379
 
1.8%
7410
 
2.0%
8400
 
1.9%
9394
 
1.9%
10367
 
1.8%
ValueCountFrequency (%)
12041
< 0.1%
11731
< 0.1%
11581
< 0.1%
11081
< 0.1%
10561
< 0.1%
10021
< 0.1%
10001
< 0.1%
8761
< 0.1%
8601
< 0.1%
8471
< 0.1%

id
Categorical

HIGH CARDINALITY
UNIFORM
UNIQUE

Distinct20707
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size161.9 KiB
gossipcop-1000240645
 
1
gossipcop-903257
 
1
gossipcop-903139
 
1
gossipcop-903138
 
1
gossipcop-903134
 
1
Other values (20702)
20702 

Length

Max length20
Median length16
Mean length16.95040325
Min length16

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique20707 ?
Unique (%)100.0%

Sample

1st rowgossipcop-1000240645
2nd rowgossipcop-1000908841
3rd rowgossipcop-1009248558
4th rowgossipcop-1012123555
5th rowgossipcop-1014383679

Common Values

ValueCountFrequency (%)
gossipcop-10002406451
 
< 0.1%
gossipcop-9032571
 
< 0.1%
gossipcop-9031391
 
< 0.1%
gossipcop-9031381
 
< 0.1%
gossipcop-9031341
 
< 0.1%
gossipcop-9031231
 
< 0.1%
gossipcop-9031181
 
< 0.1%
gossipcop-9031171
 
< 0.1%
gossipcop-9031121
 
< 0.1%
gossipcop-9031071
 
< 0.1%
Other values (20697)20697
> 99.9%

Length

2021-11-08T15:37:00.895912image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
gossipcop-10002406451
 
< 0.1%
gossipcop-11569259671
 
< 0.1%
gossipcop-10121235551
 
< 0.1%
gossipcop-10143836791
 
< 0.1%
gossipcop-10146165591
 
< 0.1%
gossipcop-10146361621
 
< 0.1%
gossipcop-10202203961
 
< 0.1%
gossipcop-10203350521
 
< 0.1%
gossipcop-10424063391
 
< 0.1%
gossipcop-10235767501
 
< 0.1%
Other values (20697)20697
> 99.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Interactions

2021-11-08T15:36:43.952863image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T15:36:17.818101image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T15:36:19.829439image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T15:36:21.439285image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T15:36:23.532055image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T15:36:25.826269image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T15:36:28.605175image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T15:36:31.443669image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T15:36:34.392658image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T15:36:37.107744image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T15:36:40.730340image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T15:36:44.260911image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T15:36:18.429249image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T15:36:19.957398image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T15:36:21.567237image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T15:36:23.714900image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T15:36:26.041204image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T15:36:28.866326image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T15:36:31.670249image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T15:36:34.627935image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T15:36:37.333249image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T15:36:41.016551image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T15:36:44.587376image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T15:36:18.613200image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T15:36:20.101442image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T15:36:21.711239image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T15:36:23.914542image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T15:36:26.266319image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T15:36:29.134451image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T15:36:31.910331image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T15:36:34.891580image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T15:36:37.612342image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T15:36:41.245271image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T15:36:44.835355image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T15:36:18.741236image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T15:36:20.245399image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T15:36:21.975282image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T15:36:24.105783image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T15:36:26.482581image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T15:36:29.382738image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T15:36:32.162374image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T15:36:35.134461image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T15:36:38.031654image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T15:36:41.529105image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T15:36:45.067104image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T15:36:18.877197image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T15:36:20.398597image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T15:36:22.151251image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T15:36:24.305028image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T15:36:26.698471image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T15:36:29.648218image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T15:36:32.423814image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T15:36:35.384892image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T15:36:38.413940image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T15:36:41.861067image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T15:36:45.362759image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T15:36:18.997250image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T15:36:20.526631image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T15:36:22.343812image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T15:36:24.505494image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T15:36:27.070831image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T15:36:29.929427image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T15:36:32.680649image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T15:36:35.630713image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T15:36:38.826814image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T15:36:42.113906image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T15:36:45.717655image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T15:36:19.141193image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T15:36:20.678617image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T15:36:22.559957image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T15:36:24.728850image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T15:36:27.330466image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T15:36:30.206773image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T15:36:33.106730image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T15:36:35.884556image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T15:36:39.058716image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T15:36:42.422532image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T15:36:46.062477image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T15:36:19.277238image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T15:36:20.838595image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T15:36:22.759461image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T15:36:25.028131image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T15:36:27.591555image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T15:36:30.472602image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T15:36:33.360134image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T15:36:36.145969image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T15:36:39.414993image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T15:36:42.776594image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T15:36:46.485169image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T15:36:19.421406image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T15:36:20.998624image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T15:36:22.950516image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T15:36:25.219394image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T15:36:27.850862image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T15:36:30.727145image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T15:36:33.611221image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T15:36:36.382788image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T15:36:39.832407image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T15:36:43.025552image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T15:36:46.947749image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T15:36:19.565451image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T15:36:21.143274image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T15:36:23.149776image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T15:36:25.418875image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T15:36:28.106343image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T15:36:30.975876image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T15:36:33.878842image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T15:36:36.629853image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T15:36:40.102823image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T15:36:43.309126image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T15:36:47.518442image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T15:36:19.709399image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T15:36:21.287295image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T15:36:23.341231image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T15:36:25.626313image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T15:36:28.350749image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T15:36:31.211838image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T15:36:34.135404image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T15:36:36.868837image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T15:36:40.451064image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T15:36:43.633805image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Correlations

2021-11-08T15:37:01.298915image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-11-08T15:37:02.110938image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-11-08T15:37:02.553429image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-11-08T15:37:03.070352image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2021-11-08T15:36:48.299118image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
A simple visualization of nullity by column.
2021-11-08T15:36:49.042575image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

labelnum_nodesnum_tweetsavg_num_retweetretweet_percnum_userstotal_propagation_timeavg_num_followersavg_num_friendsavg_time_diffperc_post_1_hourusers_10hid
0fake1161100.0454550.051724611.525941e+0920970.5652171149.0260877.437060e+050.99137956gossipcop-1000240645
1fake530.3333330.40000031.485491e+09158959.750000791.7500006.278000e+030.2000002gossipcop-1000908841
2fake320.0000000.33333311.495247e+09317729.000000723.0000000.000000e+000.3333331gossipcop-1009248558
3fake15100.4000000.333333141.496761e+0926939.0000003446.9285712.765667e+030.4666677gossipcop-1012123555
4fake30220.3181820.266667211.530403e+0930835.9655175045.8620691.241908e+040.16666711gossipcop-1014383679
5fake8346190.3457190.2577947291.536210e+0946004.3817531616.2809123.230924e+040.00959242gossipcop-1014616559
6fake2071910.0785340.0772951791.510127e+0916406.5873791466.0679618.888193e+040.39613578gossipcop-1014636162
7fake10028570.1680280.1447116551.530470e+0926516.0389611985.4115883.416425e+060.965070502gossipcop-1020220396
8fake16118550.8830410.46927414951.534299e+099555.5223601876.9155288.945355e+040.973929720gossipcop-1020335052
9fake210.0000000.50000011.535368e+0942.00000014.0000000.000000e+000.5000001gossipcop-1023576750

Last rows

labelnum_nodesnum_tweetsavg_num_retweetretweet_percnum_userstotal_propagation_timeavg_num_followersavg_num_friendsavg_time_diffperc_post_1_hourusers_10hid
20697real34330.0000000.029412241.533639e+091182.6666671157.2727270.00.38235322gossipcop-955991
20698real47460.0000000.021277371.533197e+09500.391304715.9347830.00.10638334gossipcop-955997
20699real15130.0769230.133333131.532969e+09858.1428571308.14285794.00.53333312gossipcop-956021
20700real22210.0000000.045455201.533001e+09939.6666671255.9047620.00.45454519gossipcop-956038
20701real44430.0000000.022727421.534531e+09293.720930452.3255810.00.27272739gossipcop-956070
20702real46450.0000000.021739431.535384e+095930.6000002673.1333330.00.21739136gossipcop-956072
20703real38370.0000000.026316361.533013e+09334.324324408.2702700.00.21052634gossipcop-956091
20704real56540.0185190.035714531.533027e+092063.4181821523.85454540383.00.16071441gossipcop-956093
20705real45440.0000000.022222431.533372e+092444.1136361686.8636360.00.17777837gossipcop-956103
20706real47460.0000000.021277441.533232e+09566.021739635.3695650.00.25531940gossipcop-956128